Latest revision as of 20:31, 9 July 2022
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Simulation of stationary energy storage systems
by Technical University of Munich
Authors:
Marc Möller, Daniel Kucevic, Nils Collath, Anupam Parlikar, Petra Dotzauer, Benedikt Tepe, Stefan Englberger, Martin Cornejo, Andreas Jossen, Holger Hesse, Maik Naumann, Nam Truong
Contact:
Martin Cornejo
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SimSES provides a library of state-the-art energy storage models by combining modularity of multiple topologies as well as the periphery of an ESS. This paper summarizes the structure as well as the capabilites of SimSES. Storage technology models based on current research for lithium-ion batteries, redox flow batteries, as well as hydrogen storage-based electrolysis and fuel cell are presented in detail. In addition, thermal models and their corresponding HVAC systems, housing, and ambient models are depicted. Power electronics are represented with AC/DC and DC/DC converters mapping the main losses of power electronics within a storage system. Additionally, auxiliary components like pumps, compressors, and HVAC are considered. Standard use cases like peak shaving, residential storage, and control reserve power provisions through dispatch of storage are discussed in this work, with the possibility to stack these applications in a multi-use scenario. The analysis is provided by technical and economic evaluations illustrated by KPIs.
Based on Python. Using Python for data processing.
Website / Documentation
Download
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 Open Source
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Electrical energy storage system
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| Sectors
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Electricity
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| Technologies
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Renewables
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| Decisions
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dispatch
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| Regions
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World
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| Geographic Resolution
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| Time resolution
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Minute
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| Network coverage
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| Model type
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Simulation
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Power flow and state of charge calculation based on time series profiles
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| Variables
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>50
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| Computation time
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27 minutes (20 years with 5 minute time step resolution)
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| Objective
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| Uncertainty modeling
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
Naumann, Maik; Truong, Cong Nam (2017): SimSES - Software for techno-economic simulation of stationary energy storage systems.
https://dx.doi.org/10.14459/2017mp1401541
Reports produced using the model
Naumann, M; Truong, C.N.; Schimpe, M.; Kucevic, D.; Jossen, A.; Hesse, H.C. (2017): SimSES: Software for techno-economic Simulation of Stationary Energy Storage Systems. In: VDE-ETG-Kongress 2017. Bonn. Preprint accepted for publication in IEEE Conference Proceedings. http://ieeexplore.ieee.org/document/8278770/
Naumann, M.; Karl, R.Ch.; Truong, C.N.; Jossen, A.; Hesse, H.C. (2015): Lithium-ion Battery Cost Analysis in PV-household Application. In: Energy Procedia 73, S. 37–47. DOI: 10.1016/j.egypro.2015.07.555
Truong, C.; Naumann, M.; Karl, R.; Müller, M.; Jossen, A.; Hesse, H. (2016): Economics of Residential Photovoltaic Battery Systems in Germany. The Case of Tesla’s Powerwall. In: Batteries 2 (2), S. 14–30. DOI: 10.3390/batteries2020014
Example research questions
Optimal system sizing and operation due to battery aging or economic results
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